{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 3 Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 Form dollar bars for E-mini S&P 500 futures:\n",
    "1. Apply a symmetric CUSUM filter (Chapter 2, Section 2.5.2.1) where the threshold is the standard deviation of daily returns (Snippet 3.1).\n",
    "2. Use Snippet 3.4 on a pandas series t1, where numDays=1.\n",
    "3. On those sampled features, apply the triple-barrier method, where ptSl=[1,1] and t1 is the series you created in point 1.b.\n",
    "4. Apply getBins to generate the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Hudson and Thames MlFinLab package\n",
    "import mlfinlab as ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import timeit\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, classification_report, confusion_matrix\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data\n",
    "data = pd.read_csv('official_data/dollar_bars.csv', nrows=40000)\n",
    "data.index = pd.to_datetime(data['date_time'])\n",
    "data = data.drop('date_time', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>cum_vol</th>\n",
       "      <th>cum_dollar</th>\n",
       "      <th>cum_ticks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-07-31 23:31:58.810</th>\n",
       "      <td>1306.00</td>\n",
       "      <td>1308.75</td>\n",
       "      <td>1301.75</td>\n",
       "      <td>1305.75</td>\n",
       "      <td>53658</td>\n",
       "      <td>70035704.75</td>\n",
       "      <td>14115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01 02:55:17.443</th>\n",
       "      <td>1305.75</td>\n",
       "      <td>1309.50</td>\n",
       "      <td>1304.00</td>\n",
       "      <td>1306.50</td>\n",
       "      <td>53552</td>\n",
       "      <td>70006277.00</td>\n",
       "      <td>15422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01 07:25:56.319</th>\n",
       "      <td>1306.75</td>\n",
       "      <td>1309.75</td>\n",
       "      <td>1304.75</td>\n",
       "      <td>1305.00</td>\n",
       "      <td>53543</td>\n",
       "      <td>70000901.00</td>\n",
       "      <td>14727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01 08:33:10.903</th>\n",
       "      <td>1305.00</td>\n",
       "      <td>1305.00</td>\n",
       "      <td>1299.00</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>53830</td>\n",
       "      <td>70094217.75</td>\n",
       "      <td>14987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01 10:51:41.842</th>\n",
       "      <td>1300.00</td>\n",
       "      <td>1307.75</td>\n",
       "      <td>1299.00</td>\n",
       "      <td>1307.75</td>\n",
       "      <td>53734</td>\n",
       "      <td>70033006.25</td>\n",
       "      <td>14499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            open     high      low    close  cum_vol  \\\n",
       "date_time                                                              \n",
       "2011-07-31 23:31:58.810  1306.00  1308.75  1301.75  1305.75    53658   \n",
       "2011-08-01 02:55:17.443  1305.75  1309.50  1304.00  1306.50    53552   \n",
       "2011-08-01 07:25:56.319  1306.75  1309.75  1304.75  1305.00    53543   \n",
       "2011-08-01 08:33:10.903  1305.00  1305.00  1299.00  1300.00    53830   \n",
       "2011-08-01 10:51:41.842  1300.00  1307.75  1299.00  1307.75    53734   \n",
       "\n",
       "                          cum_dollar  cum_ticks  \n",
       "date_time                                        \n",
       "2011-07-31 23:31:58.810  70035704.75      14115  \n",
       "2011-08-01 02:55:17.443  70006277.00      15422  \n",
       "2011-08-01 07:25:56.319  70000901.00      14727  \n",
       "2011-08-01 08:33:10.903  70094217.75      14987  \n",
       "2011-08-01 10:51:41.842  70033006.25      14499  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Apply a symmetric CUSUM filter (Chapter 2, Section 2.5.2.1) where the threshold is the standard deviation of daily returns (Snippet 3.1).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute daily volatility\n",
    "vol = ml.util.get_daily_vol(close=data['close'], lookback=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vol.plot(figsize=(14, 7), title='Volatility as caclulated by de Prado')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply Symmetric CUSUM Filter and get timestamps for events\n",
    "# Note: Only the CUSUM filter needs a point estimate for volatility\n",
    "cusum_events = ml.filters.cusum_filter(data['close'], threshold=vol.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use Snippet 3.4 on a pandas series t1, where numDays=1.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-08-01 10:51:41.842   2011-08-02 10:52:48.191\n",
       "2011-08-01 13:36:46.215   2011-08-02 13:37:28.849\n",
       "2011-08-01 14:00:21.448   2011-08-02 14:04:29.869\n",
       "2011-08-01 14:03:22.782   2011-08-02 14:04:29.869\n",
       "2011-08-01 14:21:49.179   2011-08-02 14:27:55.423\n",
       "Name: date_time, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute vertical barrier\n",
    "vertical_barriers = ml.labeling.add_vertical_barrier(cusum_events, data['close'])\n",
    "vertical_barriers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**On those sampled features, apply the triple-barrier method, where ptSl=[1,1] and t1 is the series you created in point 1.b.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jackal08/anaconda3/envs/test_pip/lib/python3.6/site-packages/mlfinlab/labeling/labeling.py:117: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  target = target.loc[t_events]\n"
     ]
    }
   ],
   "source": [
    "triple_barrier_events = ml.labeling.get_events(close=data['close'],\n",
    "                                               t_events=cusum_events,\n",
    "                                               pt_sl=[1, 1],\n",
    "                                               target=vol,\n",
    "                                               min_ret=0.01,\n",
    "                                               num_threads=1,\n",
    "                                               vertical_barrier_times=vertical_barriers,\n",
    "                                               side_prediction=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "                                             t1      trgt\n",
       "2011-08-04 01:57:00.466 2011-08-04 10:27:24.326  0.011841\n",
       "2011-08-04 09:08:28.542 2011-08-04 12:43:40.327  0.012108\n",
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      ]
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    "triple_barrier_events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ml.labeling.get_bins(triple_barrier_events, data['close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                              ret      trgt  bin\n",
       "2011-08-04 01:57:00.466 -0.016254  0.011841   -1\n",
       "2011-08-04 09:08:28.542 -0.012762  0.012108   -1\n",
       "2011-08-04 10:27:24.326 -0.011686  0.011682   -1\n",
       "2011-08-04 12:29:39.619 -0.011907  0.011022   -1\n",
       "2011-08-04 19:44:06.743 -0.012887  0.011423   -1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    255\n",
       "-1    207\n",
       " 0    198\n",
       "Name: bin, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels['bin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#### 3.2 From exercise 1, use Snippet 3.8 to drop rare labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_labels = ml.labeling.drop_labels(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(660, 3)\n",
      "(660, 3)\n"
     ]
    }
   ],
   "source": [
    "print(labels.shape)\n",
    "print(clean_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#### 3.3 Adjust the getBins function (Snippet 3.5) to return a 0 whenever the vertical barrier is the one touched first.\n",
    "This change was made inside the module CoreFunctions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#### 3.4 Develop a trend-following strategy based on a popular technical analysis statistic (e.g., crossing moving averages). For each observation, themodel suggests a side, but not a size of the bet.\n",
    "\n",
    "1. Derive meta-labels for pt_sl = [1,2] and t1 where num_days=1. Use as trgt the daily standard deviation as computed by Snippet 3.1.\n",
    "2. Train a random forest to decide whether to trade or not. Note: The decision is whether to trade or not, {0,1}, since the underllying model (the crossing moveing average has decided the side{-1, 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This question is answered in the notebook: 2019-03-06_JJ_Trend-Following-Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "#### 3.5 Develop a mean-reverting strategy based on Bollinger bands. For each observation, the model suggests a side, but not a size of the bet.\n",
    "\n",
    "* (a) Derive meta-labels for ptSl = [0, 2] and t1 where numDays = 1. Use as trgt the daily standard deviation as computed by Snippet 3.1.\n",
    "* (b) Train a random forest to decide whether to trade or not. Use as features: volatility, seial correlation, and teh crossinmg moving averages.\n",
    "* (c) What is teh accuracy of prediction from the primary model? (i.e. if the secondary model does not filter the bets) What are the precision, recall and FI-scores?\n",
    "* (d) What is teh accuracy of prediction from the primary model? What are the precision, recall and FI-scores?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This question is answered in the notebook: 2019-03-07_BBand-Question"
   ]
  }
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